FaaSMoE: A Serverless Framework for Multi-Tenant Mixture-of-Experts Serving
Mixture-of-Experts (MoE) models offer high capacity with efficient inference cost by activating a small subset of expert models per input. However, deploying MoE models requires all experts to reside in memory, creating a gap between the resource used by activated experts and the provisioned resources. This underutilization is further pronounced in multi-tenant scenarios. In this paper, we propose FaaSMoE, a multi-tenant MoE serving architecture built on Function-as-a-Service (FaaS) platforms. FaaSMoE decouples the control and execution planes of MoE by deploying experts as stateless FaaS functions, enabling on-demand and scale-to-zero expert invocation across tenants. FaaSMoE further supports configurable expert granularity within functions, trading off per-expert elasticity for reduced invocation overhead. We implement a prototype with an open-source edge-oriented FaaS platform and evaluate it using Qwen1.5-moe-2.7B under multi-tenant workloads. Compared to a full-model baseline, FaaSMoE uses less than one third of the resources, demonstrating a practical and resource-efficient path towards scalable MoE serving in a multi-tenant environment.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
ServerlessT2I: Efficient Text-to-Image Workflow Serving on a Serverless Platform
Text-to-image (T2I) workflows are increasingly deployed on serverless platforms because users often compose customized workflows and invoke them intermittently. Existing platforms typically deploy each workflow as an opa…
Optimizing Distributed Deployment of Mixture-of-Experts Model Inference in Serverless Computing
With the advancement of serverless computing, running machine learning (ML) inference services over a serverless platform has been advocated, given its labor-free scalability and cost effectiveness. Mixture-of-Experts (M…
Bayesian OptimizationCPUMixture-of-ExpertsArchitectural Implications of Function-as-a-Service Computing
Serverless computing is a rapidly growing cloud application model, popularized by Amazon’s Lambda platform. Serverless cloud services provide fine-grained provisioning of resources, which scale automatically with user de…
MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning
Low-rank adaptation (LoRA) and its mixture-of-experts (MOE) variants are highly effective parameter-efficient fine-tuning (PEFT) methods. However, they introduce significant latency in multi-tenant settings due to the Lo…
MathMixture-of-Expertsparameter-efficient fine-tuningRemoe: Towards Efficient and Low-Cost MoE Inference in Serverless Computing
Mixture-of-Experts (MoE) has become a dominant architecture in large language models (LLMs) due to its ability to scale model capacity via sparse expert activation. Meanwhile, serverless computing, with its elasticity an…
Semantic Similarity